AI agents in contracting: digital colleagues for legal and commercial teams 

For decades, contracting has been defined by manual processes, document-heavy workflows, and a high risk of error. As a result, it required a disproportionate amount of time and attention from legal, procurement, and commercial teams. Traditional automation provided meaningful efficiency gains, but it largely focused on accelerating tasks rather than transforming decision-making.  

Today, AI agents are ushering contracting into a fundamentally new era. Acting as digital colleagues, these agents move contract lifecycle management (CLM) beyond faster workflows into the realm of self-directing intelligence—proactively interpreting intent, identifying risk, and driving outcomes. The resulting advances in speed, accuracy, and commercial insight are beyond anything that automation alone could deliver.

What are AI agents for CLM?

AI agents are autonomous (or semi-autonomous) software entities that perceive their environment, make decisions, learn from these decisions, and take actions to achieve specific goals. In the context of CLM, they can review contracts, surface risks, trigger workflows, and keep obligations on track—transforming contracting into an intelligent, automated, insight-driven discipline.  

5 ways AI agents are reshaping contracting

AI agents are redefining how contracting work gets done, shifting it from reactive execution to proactive, intelligence-driven orchestration. Following are five practical ways agentic AI is delivering tangible value across the contracting ecosystem.

1. Intelligent auto-drafting with playbooks

AI agents can assemble first drafts of contracts by drawing on preapproved clause libraries and rule-driven playbooks—leading to shorter cycle times and less manual editing. This gives salespeople a clean, compliant starting point while ensuring legal teams get the consistency they need.

When this capability is paired with dynamic rules that adapt to the specifics of each deal, the results are even more powerful. For example, an AI agent initiating a new MSA for a California-based customer can automatically apply CCPA-compliant privacy language, select indemnity terms aligned to deal value, insert the appropriate governing law, and tailor service-level provisions based on the products being sold—all without manual intervention.  

2. Negotiation intelligence and real-time guidance

During redlining, AI agents act as intelligent negotiation copilots. They flag non-standard or risky language, recommend alternative clauses, highlight deviations from policy, and surface potential negotiation bottlenecks before they escalate. The experience is akin to having a highly informed junior attorney embedded in every negotiation—one that has institutional memory across the entire contract portfolio.

3. Proactive renewal and obligation management

AI agents continuously monitor contract terms that are often overlooked, including renewal windows, auto-renew clauses, pricing escalators, supplier obligations, and customer commitments. With agentic AI, stakeholders no longer need to rely on bulky spreadsheets, inbox reminders, or tribal knowledge. Instead, they get timely, proactive alerts that keep them ahead of deadlines and decisions—leading to reduced compliance risk, fewer last-minute scrambles, and less likelihood of revenue leakage or unfavourable renewals.

4. Autonomous workflow orchestration

AI agents keep contracts flowing by automatically orchestrating each step of the workflow, eliminating manual handoffs and the constant need to chase down approvers. Picture this: a sales rep submits a new MSA for approval. Based on the deal size, the AI agent routes the contract to the VP-level approver, eliminating the need for manual triage. Later in the process, the negotiation shows signs of stalling when the customer goes quiet for a week. The AI agent escalates the matter to the account executive and legal lead, providing context on what’s holding things up.

By coordinating these steps automatically, AI agents reduce cycle time, eliminate bottlenecks, and improves visibility—eliminating the all-too-familiar “Where is this stuck?” problem that often slows deals down.  

5. Portfolio-level insights

By analyzing thousands of agreements across customers, suppliers, regions, and deal types, AI agents uncover patterns that are impossible to detect manually. For example:

• Negotiation patterns: Agents might detect that 80% of customers push back on a specific indemnity clause and suggest that it be updated in the playbook to avoid continued friction.
• Fallback terms: Agents might point out that a certain fallback liability cap is accepted in 95% of deals. Legal could add this to the library of preapproved clauses to accelerate future negotiations.

These kinds of insights give leaders a data-driven view of how contracting actually works across the business—not just how they think it works. Together, these capabilities demonstrate how AI agents are evolving contracting from a transactional function into a strategic, intelligence-led discipline.

Common pitfalls and how to avoid them

AI agents are powerful, but they’re not magic. Their effectiveness in CLM depends on several foundational factors.

1. Data quality: If your clause library is outdated or your contracts are scattered across systems, AI agents will struggle. Clean, centralized, standardized data is critical.
2. Trust and transparency: Legal teams need to understand why an AI agent made a recommendation. Black-box suggestions won’t fly in regulated or high-risk environments.
3. Integration with existing systems: AI agents must plug into CRM, ERP, procurement systems, document repositories, and identity and access controls. Disconnected AI is just noise.
4. Governance and guardrails: Without proper governance, automation can accelerate bad processes. Organizations need clear policies around:

• What AI agents can automate
• What requires human review
• How recommendations are logged
• How models are monitored

How to pilot AI agents in contracting

It’s tempting to jump straight into deploying AI agents, but successful adoption requires a deliberate, phased approach. Before introducing autonomous intelligence into contracting workflows, organizations must address foundational considerations around scope, data quality, governance, and trust.

Step 1: Start with high value, low risk use cases

Begin with scenarios that deliver immediate, visible impact without adding unnecessary complexity. Ideal early candidates include NDA auto-drafting, renewal and obligation reminders, clause deviation detection, and playbook-driven negotiation guidance. These use cases allow teams to experience value quickly while maintaining control.

Step 2: Clean and standardize contract data

AI agents are only as effective as the information they rely on. Consolidate contract repositories, modernize clause libraries, define fallback positions, and clearly document approval rules. This foundational work ensures agents operate from a consistent, trusted source of truth.

Step 3: Establish human-in-the-loop reviews and escalation pathways

Rather than granting full autonomy from day one, it’s a good idea to introduce AI-generated recommendations gradually. In these early stages, legal and procurement teams should review, validate, and refine agents’ actions and outputs. This approach builds confidence, improves model performance through feedback, and creates a controlled path toward greater autonomy over time.  

Step 4: Measure impact

To understand how AI agents are improving contract processes, it’s important to track clear, outcome-oriented metrics such as:

• Cycle time reduction
• Redline volume and negotiation intensity
• Approval bottlenecks
• Renewal capture rates
• Risk and policy deviations

These insights help quantify value, reinforce stakeholder confidence, and strengthen the business case for broader AI investment.

Step 5: Expand to more complex agreements

Once trust and governance are firmly established, extend AI agent capabilities to agreements with greater complexity—like MSAs, SOWs, supplier contracts, and customer-facing agreements—which have even more strategic and financial upside.

Taken together, this structured approach allows organizations to move from experimentation to scale—transforming AI agents from promising tools into dependable digital colleagues across the contracting lifecycle.

What CLM and contract teams should do now

If you’re evaluating or implementing AI agents, focus on these immediate actions:

1. Modernize your playbooks. Clear rules = better AI recommendations.

2. Centralize your contract repository. A single source of truth is nonnegotiable.

3. Standardize clauses and templates. AI thrives on consistency.

4. Map your current workflows. Identify where automation can remove friction.

5. Build a cross-functional AI governance group. Include legal, procurement, IT, security, and operations. By working closely with contracting stakeholders and gathering feedback, you can identify new and creative ways to leverage agentic AI safely and securely.

6. Start small, learn fast. Pilot → validate → scale.

The future: AI agents as core members of your contracting team

AI agents won’t replace legal or procurement professionals, but they will fundamentally reshape their work—taking on repetitive, time-consuming tasks so the team can spend more time on strategy, negotiation, and relationship management.  

And the time to act is now. Organizations that are proactive in adopting AI agents will gain a clear advantage in speed, accuracy, and risk management. Waiting could mean falling behind in a rapidly evolving, AI-driven economy.  

By adopting AI agents now, organizations position themselves to lead rather than follow.

The partnership that makes AI-driven CLM work

AI-driven CLM is not a point solution—it is an enterprise capability that must be designed, governed, and adopted with intention. Successful organizations recognize that technology alone is not enough. They align data, process, people, and operating models to ensure AI delivers trusted outcomes at scale.

Conga and Forsys have forged a compelling partnership that integrates these dimensions into a cohesive framework. Conga delivers AI-powered CLM and contract intelligence capabilities, while Forsys brings the assessment and advisory, rev ops engineering, data mastery, and enterprise-grade integration expertise needed to deploy them at scale.  

By incorporating AI into their contract lifecycle management ecosystem, our customers have seen impressive results:

• 200 hours on average saved per attorney per year by reducing contract review
• 90% reduced manual data entry with AI  
• 55% improved compliance with contract review

The question is no longer whether AI belongs in contracting, but how quickly it can be operationalized to drive smarter, faster, and lower-risk outcomes. If you’re ready to turn contracts into a source of intelligence and advantage, the time to act is now.

Request a demo to see how we can help you automate review, mitigate risks, and accelerate your contract lifecycle.